{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-11-27T14:15:31.636346400Z",
     "start_time": "2023-11-27T14:15:31.616699300Z"
    }
   },
   "outputs": [],
   "source": [
    "from paddlenlp import Taskflow\n",
    "from gensim.corpora import Dictionary\n",
    "import logging\n",
    "from warnings import filterwarnings\n",
    "import glob\n",
    "import re\n",
    "import pandas as pd\n",
    "import os\n",
    "import gensim\n",
    "from gensim.models import LdaModel\n",
    "from gensim.models import CoherenceModel\n",
    "import pyLDAvis.gensim_models\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "filterwarnings('ignore', category=DeprecationWarning)\n",
    "logging.basicConfig(level=logging.ERROR)\n",
    "class ZhLDA:\n",
    "    def __init__(self):\n",
    "        self.nlp = Taskflow(\"word_segmentation\")\n",
    "        \n",
    "    def processing(self,text):\n",
    "        res = self.nlp(text)\n",
    "        return [i.lower() for i in res if i not in my_stop_word and i not in stop_words and len(i) > 2 and not re.findall(\"\\W\",i)]\n",
    "    \n",
    "    # 构建LDA模型\n",
    "    @staticmethod\n",
    "    def build_lda_model(corpus, dictionary, num_topics):\n",
    "        lda_model = LdaModel(corpus=corpus, id2word=dictionary, num_topics=num_topics)\n",
    "        return lda_model\n",
    "    \n",
    "    \n",
    "    # 寻找最佳主题数量\n",
    "    def find_best_topic_number(self, corpus, dictionary, texts, max_topics=10):\n",
    "        coherence_scores = []\n",
    "        perplexity_scores = []\n",
    "    \n",
    "        for num_topics in tqdm(range(1, max_topics + 1)):\n",
    "            lda_model = self.build_lda_model(corpus, dictionary, num_topics)\n",
    "            coherence_model = CoherenceModel(model=lda_model, texts=texts, dictionary=dictionary, coherence='c_v')\n",
    "            coherence_score = coherence_model.get_coherence()\n",
    "            coherence_scores.append(coherence_score)\n",
    "    \n",
    "            perplexity_score = lda_model.log_perplexity(corpus)\n",
    "            perplexity_scores.append(perplexity_score)\n",
    "    \n",
    "        # 画图展示\n",
    "        plt.figure(figsize=(12, 6))\n",
    "        plt.subplot(1, 2, 1)\n",
    "        plt.plot(range(1, max_topics + 1), coherence_scores, marker='o')\n",
    "        plt.title('Coherence Score vs. Number of Topics')\n",
    "        plt.xlabel('Number of Topics')\n",
    "        plt.ylabel('Coherence Score')\n",
    "    \n",
    "        plt.subplot(1, 2, 2)\n",
    "        plt.plot(range(1, max_topics + 1), perplexity_scores, marker='o')\n",
    "        plt.title('Perplexity Score vs. Number of Topics')\n",
    "        plt.xlabel('Number of Topics')\n",
    "        plt.ylabel('Perplexity Score')\n",
    "    \n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "    \n",
    "        # 寻找最佳主题数量\n",
    "        best_num_topics = range(1, max_topics + 1)[coherence_scores.index(max(coherence_scores))]\n",
    "        return best_num_topics\n",
    "\n",
    "    \n",
    "    def run(self,document):\n",
    "        # 分词\n",
    "        print(\"中文分词中\")\n",
    "        processed_texts = [self.processing(i) for i in document]\n",
    "        # 构建LDA模型所需的语料库和字典\n",
    "        print(\"构建语料库中\")\n",
    "        dictionary = gensim.corpora.Dictionary(processed_texts)\n",
    "        corpus = [dictionary.doc2bow(text) for text in processed_texts]\n",
    "        best_num_topics = self.find_best_topic_number(corpus, dictionary, processed_texts, max_topics=10)\n",
    "\n",
    "        # 构建最终的LDA模型\n",
    "        final_lda_model = self.build_lda_model(corpus, dictionary, best_num_topics)\n",
    "        res = final_lda_model.print_topics(num_topics=best_num_topics, num_words=50)\n",
    "        for topic,words in res:\n",
    "            print(f\"主题{topic+1}\",\", \".join(re.findall('\"(.*?)\"',words)))\n",
    "        \n",
    "        # 可视化主题\n",
    "        pyLDAvis.enable_notebook()\n",
    "        vis = pyLDAvis.gensim_models.prepare(final_lda_model, corpus, dictionary)\n",
    "        pyLDAvis.save_html(vis,save_path+\"中文主题建模结果.html\")\n",
    "        print(\"中文主题建模结果已保存至\",save_path+\"中文主题建模结果.html\")\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [],
   "source": [
    "my_stop_word = [\"recommended\",\"skills\",\"position\",\"equal\"]+['role', 'work address', 'education requirement', 'work time', 'year', 'benefit', 'position', 'work atmosphere', 'skill', 'description', 'job requirement', 'responsibility', 'requisition', 'personality', 'professional requirement', 'post', 'salary', 'you', 'other instruction', 'work location', 'job description', 'recommend', 'daily work', 'overview', 'today', 'job', 'skill requirement', 'welfare benefit', 'category', 'job benefit', 'job qualification', 'job responsibility', 'company introduction',\"overview\"] + ['Position', 'Responsibilities', 'Description', 'Benefits', 'Job Requirements', 'Job Responsibilities', 'Job Requirements', 'Job Description', 'Work Location', 'Recommended Skills', 'Professional Requirements', 'Skill Requirements', 'Education Requirements', 'Other Instructions', 'Company Introduction', 'Work Address', 'Work Hours', 'Personality', 'Skills', 'Experience', 'Company Introduction', 'Job Responsibilities', 'Job Requirements', 'Job Benefits', 'Welfare Benefits', 'Qualification', 'Work Atmosphere', 'Salary Benefits', 'Qualification', 'Daily Work'] + [\"At least a\",\"Requisition ID\",\"Today\",\"Summary\",\"g\",\"JOB SUMMARY\",\"Job Description : JOB SUMMARY\",\"tomorrow\", \"years\", \"include\",\"including\"]\n",
    "stop_words = open(\"../Data/stop_words.txt\", encoding=\"utf-8\").read().split(\"\\n\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-27T14:15:31.705348300Z",
     "start_time": "2023-11-27T14:15:31.629346700Z"
    }
   },
   "id": "573158a152a0aca"
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "当前网站: boss直聘\n",
      "当前网站: CareerBuilder\n",
      "当前网站: CIA\n",
      "当前网站: DNI\n",
      "当前网站: linkedin\n",
      "当前网站: Simplyhired\n",
      "当前网站: 智联招聘\n",
      "中文数据:\t 3434 条数据\n"
     ]
    }
   ],
   "source": [
    "files = glob.glob(\"../Data/数据清洗/*\")\n",
    "save_path = \"../Data/LDA主题建模/\"\n",
    "if not os.path.exists(save_path):\n",
    "    os.mkdir(save_path)\n",
    "Zh_data = list()\n",
    "for file in files:\n",
    "    file_name = file.replace('\\\\', '/').split('/')[-1].split('.')[0]\n",
    "    print(f\"当前网站: {file_name}\")\n",
    "    df = pd.read_csv(file, index_col=0)\n",
    "    # 删除重复行\n",
    "    df = df.drop_duplicates(keep='last')\n",
    "    # 去掉缺失值\n",
    "    df = df.dropna(subset=['职位', '任职要求'])\n",
    "    if re.search('[\\u4e00-\\u9fa5]', file_name):  # 中文网站\n",
    "        Zh_data += df['任职要求'].tolist()\n",
    "    else:  # 英文网站\n",
    "        pass\n",
    "print(\"中文数据:\\t\",len(Zh_data), \"条数据\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-27T14:15:31.846359600Z",
     "start_time": "2023-11-27T14:15:31.680349300Z"
    }
   },
   "id": "e85cdc21598349c5"
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "中文分词中\n",
      "构建语料库中\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/10 [00:00<?, ?it/s][2023-11-27 22:21:05,148] [ WARNING] ldamodel.py:959 - too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n",
      " 10%|█         | 1/10 [00:07<01:05,  7.33s/it][2023-11-27 22:21:12,474] [ WARNING] ldamodel.py:959 - too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n",
      " 20%|██        | 2/10 [00:18<01:16,  9.62s/it][2023-11-27 22:21:23,710] [ WARNING] ldamodel.py:959 - too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n",
      " 30%|███       | 3/10 [00:30<01:14, 10.66s/it][2023-11-27 22:21:35,600] [ WARNING] ldamodel.py:959 - too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n",
      " 40%|████      | 4/10 [00:40<01:02, 10.45s/it][2023-11-27 22:21:45,747] [ WARNING] ldamodel.py:959 - too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n",
      " 50%|█████     | 5/10 [00:51<00:52, 10.47s/it][2023-11-27 22:21:56,248] [ WARNING] ldamodel.py:959 - too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n",
      " 60%|██████    | 6/10 [01:01<00:42, 10.54s/it][2023-11-27 22:22:06,898] [ WARNING] ldamodel.py:959 - too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n",
      " 70%|███████   | 7/10 [01:12<00:31, 10.56s/it][2023-11-27 22:22:17,515] [ WARNING] ldamodel.py:959 - too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n",
      " 80%|████████  | 8/10 [01:22<00:21, 10.50s/it][2023-11-27 22:22:27,905] [ WARNING] ldamodel.py:959 - too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n",
      " 90%|█████████ | 9/10 [01:33<00:10, 10.60s/it][2023-11-27 22:22:38,722] [ WARNING] ldamodel.py:959 - too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n",
      "100%|██████████| 10/10 [01:43<00:00, 10.39s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2023-11-27 22:22:49,516] [ WARNING] ldamodel.py:959 - too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "主题1 五险一金, 工作经验, 经验者, 责任心, 数据库, 医疗保险, 计算机, 逻辑思维, 国内外, 经济学, 互联网, python, 研究生, 宏观经济, 分析师, excel, 统计学, 学习能力, sql, office, 产业链, 数据处理, 新能源, ppt, 上市公司, 工作经验者, 3年以上, 总经理, 可行性, 执行力, 经纪人, 不定期, cfa, 基本面, 优秀者, 1年以上, 理工科, 2年以上, 5年以上, 洞察力, project, 工作经历, 信息技术, 福利待遇, 市场分析, 应届生, 信息化, 公积金, cpa, 吃苦耐劳\n",
      "主题2 五险一金, 工作经验, business, 数字化, team, 解决方案, work, experience, 医疗保险, 上市公司, 房地产, sales, 供应链, 逻辑思维, management, office, excel, 工作经历, 责任心, support, customer, quality, development, project, communication, 互联网, market, 5年以上, clients, 研究生, product, 3年以上, good, strong, 2年以上, ability, 计算机, working, responsible, client, ppt, knowledge, 负责人, degree, 工作经验者, ipo, data, 会计师, english, process\n",
      "主题3 五险一金, 工作经验, 医疗保险, 责任心, 3年以上, 经验者, 解决方案, excel, 计算机, ppt, 逻辑思维, 工作经验者, word, 统计学, 2年以上, 国内外, 研究生, 责任感, 金融机构, 互联网, 执行力, office, esg, 5年以上, 上市公司, 学习能力, python, 可行性, 产业链, 可持续, 研究报告, 理工科, 各部门, 35周岁, 房地产, 吃苦耐劳, sql, 负责人, 1年以上, 新产品, 经济学, 数据库, 毕业生, 洞察力, 宏观经济, 市场分析, 投融资, 竞争对手, 社会学, 咨询公司\n",
      "主题4 五险一金, 经纪人, management, project, 工作经验, work, experience, support, business, 医疗保险, 职业化, 消费者, team, research, ability, market, 优秀者, 竞争力, related, 国内外, 试用期, good, 房地产, 总经理, projects, cost, development, 8000元, strong, 经验者, english, 致力于, industry, data, 12个月, 工作时间, 逻辑思维, excellent, communication, study, knowledge, 上市公司, analysis, 研究生, 责任心, 专业化, 竞争对手, office, 咨询服务, 三十天\n",
      "中文主题建模结果已保存至 ../Data/LDA主题建模/中文主题建模结果.html\n"
     ]
    }
   ],
   "source": [
    "ZhLDA().run(Zh_data)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-27T14:22:54.786888100Z",
     "start_time": "2023-11-27T14:15:31.846359600Z"
    }
   },
   "id": "6a680a4bdea3d0a7"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
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}
